Greenhouse gases emission reduction for electric power generation sector by efficient dispatching of thermal plants integrated with renewable systems

This research aims to contribute in developing a mathematical model for the composite probabilistic energy emissions dispatch (CPEED) with renewable energy systems, and it proposes a novel framework, based on an existing astute black widow optimization (ABWO) algorithm. Renewable energy power generation technology has contributed to pollution reduction and sustainable development. Therefore, this research aims to explore the CPEED problem in the context of renewable energy generation systems to enhance the energy and climate benefits of the power systems. Five benchmark test systems, combined with conventional thermal power plants and renewable energy sources such as wind and solar, are considered herein to obtain the optimum solution for cost and pollutant emission by using the ABWO approach. The ascendancy is not limited to environmental impacts, but it also provides the diversification of energy supply and reduction of reliance on imported fuels. As a result, the research findings contribute in lowering the cost of fuel and pollutant emissions, correlated with electricity generation systems, while increasing the renewable energy usage and penetration. Finally, the performance and efficacy of the designed scheme have been fully validated by comprehensive experimental results and statistical analyses.

www.nature.com/scientificreports/ networks 42 , bi-stage approach 43 and decentralized cooperative techniques 44 have been used to model the RES uncertain behavior in energy grids. To bolster the differential evolutionary algorithm's adaptive capability, the author in 45 proposed a self-adaptive variable search approach as well as a local search operator, for solving the EELD problem in high dimensions, containing highly nonlinear terms, non-smoothness, and non-convexity. However, the work ignores the fact that such algorithms are prone to local values and pushed to their limits, limiting their utility in solving the CPEED problem. In the light of the growing share of modern power generation systems, it is necessary to investigate the impact of photo-voltaic and wind power production on the energy and environmental benefits of power systems. The conventional methods like [46][47][48][49][50] have several research gaps as these methods have not accounted a comprehensive, generic, and highly conflicting optimization problem of CPEED by incorporating wind and solar generation uncertainties, load demand limits, generating capacity limitations, and ramp-rate impacts of generation machines. In addition, these methods can also be improved for better cost value and for better convergence time.
To bridge the above-mentioned research gaps in the literature, we redesign the EELD objective model by taking into account both wind power generation and photo voltaic power generation as RES, in addition to the conflicting relationship between economic and environmental benefits over time. Integration of RES into conventional energy hubs will be beneficial in numerous ways. First, it contributes to the reduction of greenhouse gases emission in power hubs in order to maintain the environment. Consideration of international emissions control agendas, such as the United Nations sustainable development goals, will be facilitated by the scheme. In addition, it will help states to deal with geopolitical concerns by lowering their dependence on imported fuels. There are two main purposes ofthe present study: First, the CPEEDmust be solved, and a competitive solution must befound to reduce power generation costs and pollutant emissions to the lowest possible levels. Second, effective solutions of CPEED must meet the non-convex, high dimensional constraints of EELD such as FOR, VLE, and power output balancing limitations with a part of renewable energy probabilistic model, featuring the combined influencing uncertainty parameter of wind and solar profiles. Our work's primary philanthropy is as follows: 1. A practical and more generic multi-objective model of CPEED is considered herein for energy hubs, integrated with RES. The presented model considers most of the real-world complex system-level constraints such as capacity constraint, ramp-rate limitation, load balancing limitation, VLE limitation of thermal plants and RES uncertainties for both solar and wind as compared to 46-49 . 2. The existing ABWO approach is applied to solve the difficult probabilistic multi-objective CPEED model with multi-dimensional restrictions associated with traditional thermal plants and integrated RES. Owing its feature of elegance and adaptability, ABWO results in effective fuel cost and pollutant emissions reductions for the considered five test systems, ranging from small-to-large scales. It is clear from the results that ABWO outperforms other approaches as compared to 51-56 . 3. The proposed techniques provide better results in terms of early convergence in the presence of non-convex, non-linear, and high-dimensional constraints due to the cannibalism mechanism in ABWO, and they offer higher optimization performance and lower computational cost. Compared to current state-of-the-art algorithms, the proposed algorithm has better convergence and execution time. The mutation step validates the equilibrium between the exploitation and exploration phases. Therefore, the proposed approach is more effective at avoiding the local solutions within a given search space as compared to 51-57 . Problem formulation and CPEED modeling. A framework for CPEED can reduce the cost of generation as well as the pollutant air emissions by scheduling the optimum generation from generating resources. There is no universal solution to obtain a single feasible optimized solution set for both models, simultaneously. These two conflicting issues yield various solution sets of Pareto optimal allocation, and choice goes to the decision-maker to attain a solution according to the priorities. The objective function of CPEED can be expressed as in Eq. (1) 58 .
where F denotes the multi-objective problem to be minimized and F TC and E TE are the total fuel cost and total emissions, respectively.
Fuel cost modeling. The model for economic generation cost can be expressed by quadratic function and is shown in Eq. (2) 59 .
where u i , v i , and w i are the fuel cost coefficients of particular thermal power plant. f i denotes the thermal generation cost, and p i is the generated power of N committed units.
Fuel cost modeling with VLE. VLE introduces the rippling effect on the cost curve of thermal power plants and makes the function as highly non-convex. The modelling of fuel cost with VLE can be shown in Eq.
(1) where f SGC represents the solar generation cost. N s and S p,k are the number of solar panels and power in megawatts, respectively.
Cost modeling of wind generation. The mathematical model for wind generation cost has the form where f WGC represents the wind generation cost. N z and W p are the number of wind plants and power in megawatts, respectively.
Total cost model with VLE. The mathematical model for total cost with VLE can be expressed as in Eq.
where F TC is the total cost as shown in Eq. (1) and the model consists of sum of all associated costs of load dispatch with VLE.
Total greenhouse gasses emission model. In this study, a comprehensive model of pollutant emissions is used to simulate the relationship between pollutant emissions and thermal power unit output, which is a quadratic function along with an exponential function. Usually, thermal power plants burn different fossil fuels to generate power, while the wind and solar energy systems are considered as clean sources because they do not emit pollutant. The emissions objective function does not need to consider the emissions characteristics of wind and photo voltaic power generation, and it can be written as mentioned in Eq. (7) 5 .
where X i , Y i , Z i , θ i and σ i are the emissions coefficients of all committed generator. Scientists have focused on two aspects of solving multi-objective problems commonly known as the priori and posterior approaches. The priori approaches reduce a multi-objective problem to a single problem by applying appropriate weights to achieve a feasible compromised solution that meets the requirements. The mathematical modelling is shown in Eq. (8) 61 .
CPEED is a complex engineering problem, and it consists of different nonlinear system limitations. To obtain the optimal set of solutions, the proposed ABWO strategy will satisfy all CPEED related constraints. The combined power generated from all sources, that is, thermal wind and solar must meet the load demand and transmission loss, as depicted in Eq. where β ij , β oi and β oo are loss coefficients matrix 62 . Ramp-rate limitation. The ramp-rate limits can be modelled for generated p i by a generator as given in Eq. (12) 59 .
where UR i and DR i are the upper and down ramp-rate limitations. Since some generating units have physical limitations, their actions are limited to specific operating regions, likely to result in input-output characteristics that are discontinuous in nature. The modeling for such type of regions in input-output characteristics is given in Eq. (16) 64 .
Wind probability density function. Wind energy generation is heavily reliant on wind speed variability.
Numerous techniques have been used to portray the randomness of wind speed characteristics. The Weibull probability density function (WPDF) is used to model the wind speed attributes, and it is used to define the stochastic feature of wind speed profiles. The mathematical expression of WPDF is as shown in Eq. (14) 65 .
where T and represent the scale and shape parameter, respectively. V CWD shows the current speed of wind in (m/s). The power output of wind is computed using the speed-power curve via Eq. (15).
where w r represents the rated wind turbine power and W p is the generated output power. V r , V in and V show the cut in/off and rated speed respectively. Solar probability density function. Unlike the wind energy, the electricity production of the photovoltaic module is heavily reliant on solar radiations, environmental temperature and module performance characteristics. In this work, we apply beta distribution (BPDF) to represent percentages or proportions of outcomes. The mathematical modelling of solar energy using BPDF is depicted in Eq. (16) 66 .
Here R and TE represent the f BPDF parameters, is the electromagnetic radiation power per area, and Ŵ is gamma function of BPDF. By using mean (µ) and standard deviation (ε) , the function can be written as follows: As stated earlier, the dependency for power generation in photo-voltaic modules relies on the solar irradiance and the temperature of cells. It can be formulated via Eq. (19).
The temperature of cells can be determined from Eq. (20), where S p (t) denotes the solar radiation incident on the photo-voltaic module at time interval t. S p(stcon) and S rad.stcon represent the solar power and radiance in the standard test conditions. χ is the coefficient of temperature while U cell and U cell.stcon are the cell temperature and temperature at standard conditions. U ambient and U normal.temp are the environmental and normal temperature, respectively. www.nature.com/scientificreports/ Traditional thermal power plants can provide electricity regardless of the weather, but RES rely on the climatic strength to generate power, making it imperative to have an accurate and reliable model to deal with the unpredictability of climate circumstances. In this study, we used beta-distribution function (16) and Weibull probability density function (14) to model the uncertainty in RES. The wind PDF is computed using (14), where T is the scaling factor and is the wind turbine blade profile shape factor. The values of T and V for the Kings park site are 3.23 and 5.8 metres per second, respectively. Once the unpredictability of the wind has been classified as a stochastic process, the output power of the wind generator can be measured as a random variable by transforming wind speed into output power. Equation (15) can be used to determine the output power of the wind based on the wind velocity W P , and can used in wind cost function Eq. (5) to compute the cost. To account for the uncertainty in the solar cost function, solar irradiance is classified as a random variable using the beta distribution. f (BPDF) (�) is the distribution function and is characterized as a random variable of solar irradiance (kw/m 2 ). Now, in order to acquire the power that the solar panel produces, we have used S P,k = S P0 × f (BPDF) (�) , which further is used in the solar cost function equation (4) to compute the cost. It is pertinent to mention here that we have formulated a complex and more practical model of the economic emission dispatch which includes practical contagious system-level limitations for example, VPLE, ramp-rate limitations and system losses as compared to the similar ABWO framework 50 . Additionally, we have also integrated the renewable energy resources in our work in the form of wind and solar sources in comparison to the mentioned research study.

Proposed computing paradigm
This section discusses the details on parameters and implementation of ABWO to resolve the CPEED problem. Hybrid power systems play a critical role in reducing reliance on imported fossil fuels and lowering atmospheric contamination. The penetration of RES in traditional energy sources introduce many challenges in power system operations. To handle the complex multi-dimensional constraint for RES integration modelling with thermal plants, we need a powerful computing framework that attains a feasible solution set by satisfying all associated constraints. Hayyolalam and Pourhaji Kazem 67 proposed a new evolutionary optimization algorithm, based on the mating behaviour of black widow spiders. It has been used to resolve a variety of scientific and engineering problems due to its ease of use, flexibility, and speed. A black widow feminine stays out of sight during the sunlight period and spins her web during the night. The widow usually lives along the same location for much of her adult life. When a female black widow is ready to mate, she sprays pheromone on specific areas of her net in order to attract the attention of the male. By reducing the attractiveness of females webs to rivals, the first male to enter the web makes females webs less appealing to them. The female eats the male during or after mating, and then she transfers the eggs to her egg sac, which she then consumes 68 . Upon hatching, the young cannibalise each other, a short time later, they leave their mothers' web and may even eat her. Only the fit and strong individuals will survive 69 . This is the global optimum of the objective function, and it is the best one. Figure 1a depicts a female black widow on her web, while Figure 1b shows a female black widow with an egg sac on her web.
In the ABWO algorithm, each spider represents a potential solution. They try to reproduce a new generation of spiders in pairs. After or during mating, female black widows eat the male black widows. Then she releases the sperms she had stored in her sperm theca into the egg sacs. After 11 days, spider lings emerge from the egg sacs and sibling cannibalism is discerned during their stay on the mother's web for a few days to a week.
(a) Female black widow on her web (b) female black widow with an egg sac problem variables must form a suitable structure. This structure is known as a chromosome in genetic algorithms and particle position in particle swarm optimization, but it is known as a widow in ABWO. Each CPEED problem in ABWO has a potential solution as a black widow spider. Each black widow spider represents a problem variable. In this paper, the structure is treated as an array to solve benchmark functions. A N var dimensional optimization problem's solution is represented by the 1 × N var array. The fitness of a widow is calculated by the fitness function. The logical steps of ABWO are as follow.
Initial population. The candidate N pop × N var size widow matrix is generated with an initial population of spiders to initiate the optimization algorithm. The parents' pairs are then randomly chosen to produce a new generation by mating the black male widow that the female eats during or after that time.
Procreate. Because the pairs are no longer dependent on one another, they begin to mate in order to produce a new generation of living creatures. A random selection of parents is made during the procreation process. Brand new generations (spider babies) are produced according to the following mathematical expression.
where O prnt (1) and O prnt (2) represent the parents and Q spider.baby (1) and Q spider.baby(2) denote spider babies or new generations, and D rv is a random vector between 0 and 1.
Cannibalism. Population decimation happens in three distinct ways during the cannibalism phase. The first type of population destruction occurs when a female spider consumes a male spider. In the second version, larger spiders consume smaller spiders. In the third category, infants consume their mothers. The cannibalism rating (CR) in ABWO is used to calculate the population's rate of survival 70 .

Mutation.
A mutation function is applied to a random subset of population members during the mutation phase. Each of the selected solutions swaps two components in the array at random. The mutation rate is used to calculate mutepop 71 .

Convergence of ABWO.
It is possible to consider three stopping conditions for ABWO algorithms, similar to other evolutionary algorithms, which are as follow.
1. The number of iterations can be predetermined. 2. Observation of the best widow's fitness value remains the same after several iterations. 3. The desired level of precision is achieved.
Parameter setting. Identifying parameters is an important step in the proposed algorithm. It's the cannibalism, procreation, and mutation rates setting. Table 1 lists the chosen values for the parameters in this paper for ABWO tuning.
Implementation of ABWO for CPEED. The computing framework for implementing ABWO for CPEED is depicted in Fig. 2, which includes objective functions, modelling equations, and an ABWO approach with performance criteria. The flowchart in Fig. 3 illustrates the problem initialization parameters and CPEED fitness stopping criterion. Algorithm 1 states the complete details for instituting ABWO for CPEED. Figure 2 depicts the working strategy of ABWO computing paradigm. All CPEED cost functions are converted into a single objective function according to the parametric setting in Table 1 and fed into the optimizer framework. The framework starts optimizing the objective function according to the flow chart in Fig. 3 and attains the best optimist set of values by satisfying all constraints in consonance with the performance indicator [51][52][53][54][55][56] . According to the parameter values presented in Table 1, a population of spiders, each of which demonstrates an ideal solution, is employed as a starting point for the proposed ABWO. This is the first step in the process, and it consists of the spiders mating and seeking to make a new breed of offspring. The purpose of this new generation is to bring the population up to date, and it will also choose two genes at random in compliance with the cannibalism and mutation plan that has been established. Once again, the population is altered in line with the values of the www.nature.com/scientificreports/ parameters, and the procedure is carried out as frequently as is required until the stopping criterion is attained (all constraints are satisfied and global optimal point is determined).  www.nature.com/scientificreports/ It is worth mentioning that the present work considers a multi-objective model for optimization by considering a large number of constraints, compared to the existing works in [46][47][48][49] . The optimization problem considers both the conventional and renewable power plants, containing both solar and wind sources, for an energy hub. In addition, system level constraints, namely, capacity constraint, ramp-rate limitation, load balancing limitation, VLE effect of thermal plants and RES uncertainties, have also been incorporated in our study. Furthermore, the optimization problem considers both the cost and the greenhouse gases emission optimization for dealing with the environmental challenges. To demonstrate the effectiveness of the suggested ABWO approach in the present work, five small-to large-scale case studies have been examined in the next section. In terms of optimal operational cost, faster convergence, and less computational time, the analysis demonstrated that ABWO performs significantly better than the existing advanced approaches, reported in the literature [51][52][53][54][55][56] . Consequently, the consideration of several constraints, different type of systems, reduction of generation cost, and minimization emission of greenhouse gases along with improvement in results in terms of optimal cost, less convergence time, and less computational resources is a significant contribution of the present work.

Simulation and results
Five benchmark test systems have been chosen to demonstrate the practicability and efficacy of ABWO in realworld applications. The obtained results from ABWO are compared to those obtained from existing state-ofthe-art meta-heuristic techniques. The required time analysis was conducted on Lenovo notebook outfitted with an Intel celeron (R) N2940 CPU running at 1.83 GHz and 4.0 GB of RAM, with MATLAB version R2017b. Five categories of experimental case studies with experimental layout shown in Fig. 4 are adopted for this research. In addition to solar and wind generators, five case studies are comprised of a varying number of traditional thermal generators. In order to evaluate the speed, agility and efficacy of the proposed algorithm, these generating units with a wide range of operating conditions, as shown in the constraints, are chosen for each test system. The ABWO algorithm is applied to both small-and large-scale benchmark systems in order to ascertain whether its controls are adequate or not for all systems integration under consideration. In order to verify the accuracy of the proposed method, the case studies at varying conditions with different numbers of generators are chosen with the primary goal of establishing validity of the proposed method. The precise details of these five case studies are discussed in detail in subsections as follow.  Tables 2 and 3 contain information on the input data for wind and PV units, respectively, while Table 4 contains information on the cost coefficients for thermal units. Each wind unit has a generating capacity of 0.8 megawatts (MW). The experimental layout for Case study-I is shown in Fig. 5. Each solar power unit has a maximum capacity of 10 MW. The bid rate for a solar power unit is 2.854 dollars per megawatt hour (MWh). The optimum power generation outputs of thermal units are shown in Fig. 6. The total demand for electricity is 1050 MW, and the optimum power output obtained by ABWO approach is shown in Fig. 6. Table 5 provides the comparison of best mean costs (which is 10017.4602 by ABWO) by satisfying all non-convex constraints with other advanced heuristic approaches. The 500 independent iterations have been performed to testify efficacy of the proposed ABWO approach. The convergence profile of the proposed ABWO computing paradigm is shown in Fig. 7. It is quite evident from Fig. 7 that the suggested computing paradigm handles the complex objective function bounded by nonlinear limitation within fine computational time as compared to other heuristic techniques in Table 5.
To compare the greenhouse gases emission of plants with the same capacity with RES, we have simulated the same power capacity hub for RES using thermal power plant as the base case via RETScreen® 72 (one of the most reliable tool developed by Canadian government for green energy design evaluation) and determined that the gross annual reduction in carbon is 40,180.5 t CO 2 , which is nearly 93% of thermal plant. Figure 8 depicts the emissions comparison of base case (thermo-electric plant) and RES generation.
Case study-II. A combination of five traditional thermal power generating units, two wind power generators and two solar PV setups is used in this case study. The non-convex constraints of VLE, capacity limits, machine     Tables 2 and 3, respectively. While the cost coefficients data for thermal units is presented in Table 6. Table 7 depicts the best mean value 1944.61 attained by ABWO for case study-II. It clearly shows that the performance of ABWO is again better as compared to other computational techniques in literature. The computational efficacy of ABWO outperforms well while handling complex system limitations of thermal power plants. Figure 9 represents the optimal combination of powers attained for case study-II, and the power generated from RES in this case study is 4.3621MW. Figure 10 depicts the convergence profile of the proposed computing framework. Here, it is evident that the proposed technique rapidly converges to the optimum solution.
Case study-III. Here, six conventional thermal units are combined with two wind and two solar units by accounting all linear and non-convex constraints. A maximum of 0.8 megawatts of energy can be extracted from a wind turbine. Each solar photo-voltaic (PV) system generates 10 MW. The bid rate for the solar power unit    Tables 2 and 3. The utility network's load demand is 1263 MW. Table 8 contains the fuel coefficient input data for thermal units. Table 9 shows the performance of ABWO with respect to other heuristic approaches. Again, the proposed computing framework performs better than the conventional computing techniques in terms of computational and fuel costs, while efficiently handling the non-convex limitations of the objective function. Figure 11 depicts the best allocated Table 6. Thermal units cost input data for case study-II.  www.nature.com/scientificreports/ power outputs for case study-III, and Fig. 12 shows the convergence profile against non-convex objective function in Eq. (6).
Case study-IV. In this case study, a medium-scale test system of fifteen traditional thermal power plants has been considered along with two solar and wind RES. The parameters of bid rate for solar and quantity of RES are same as in the previous case studies. The input data for RES is provided in Tables 2 and 3, while the thermal unit fuel input data is tabulated in Table 10. Load demand power for this case study is accounted as 2630 MW. Table 11 shows that the best mean cost attained by ABWO is much finer as compare with the traditional heuristic techniques. Another important factor for soft computing framework is the computational budget. It can also be verified from Table 11 that the computational time required for Case study-IV is much better than the heuristic approaches. Figure 13 depicts the optimum power allocation by ABWO, and Fig. 14 represents the convergence profile for Case Study IV.
Case study-V. This case study has been considered with the assumption that the generation from RES is zero because of atmospheric conditions (or system is disconnected from power grid for maintenance purposes). The conventional thermal plants will share all load of network area with restrictions of emissions imposed by emissions regulatory authorities such as KOYOTO PROTOCOL 73 , EPA 74 and European Commission 75 for sustainable environment. A gas fueled plant is selected with non-convex and linear constraints. The emissions and fuel coefficient data is tabulated in Table 12 with load demand 850MW. The two highly conflicting objective functions in Eq. (8) are chosen. The ABWO outperforms to attain the optimum fuel cost and emissions while handling constraints presented in Eqs. (9) to (16). The ABWO framework is used to simultaneously minimize the total cost of fuel and emissions. Table 13 outlines the fuel costs and minimum emissions obtained by the proposed approach of ABWO, NSGA-II 76 and TABU 77 , for this case study. The suggested approach can be found from Table 13, which is less expensive than other comparative approaches with respect to fuel and emissions. The fuel minimization problem obtained values are 8241.0821 and 0.0953 lb/h, respectively, for the minimum fuel cost and emissions.   Table 13 is preferable, compared to the other approaches. This is due to the true modelling of the black widow capacities to determine the best solution. ABWO looks for newly updated solutions around the region, which allows enough exploitation.

Statistical analysis.
Statistical analyses are the process by which trends, patterns and relations are determined through use of statistical data. Such analyses constitute a crucial instrument of data analysis, used by scientists on a limited objective basis. A comprehensive statistical analysis is used to assess the reliability of stochastic algorithms. Table 14 shows the trends and patterns for all case studies investigated for this work. Table 15 depicts the contribution of RES in power grid by sharing load of thermal power plants, hence, reducing the carbon and other pollutant oxides. The RES contribution against different loads is provided for each case study.       www.nature.com/scientificreports/ The proposed scheme has various advantages as compared to other optimization frameworks. Some of elegant features as compared to the conventional approaches are early convergence and the ability to achieve an optimal cost and emissions, which saves an energy hub's annual revenue. Additionally, the provided scheme can achieve optimal solution in the fewest iterations subject to different restrict system limitations. With a solid balance between exploitation and exploration, the framework is less likely to get stuck in local minima compared to other advance evolutionary-based algorithms.

Conclusions
The purpose of this study was to consider an existing constrained multi-objective population based optimization technique known as the ABWO computing paradigm for finding the optimal solutions to multi-objective CPEED problems with RES penetration by minimizing two conflicting total cost and environmental emissions objectives. In addition, the feasibility and reliability of the proposed techniques were verified for five test cases, including small-, medium-and large-scales systems. The research outcomes contributed to the improvement of     Figure 15. ABWO performance for case study-V. www.nature.com/scientificreports/ the power system for attaining environmental and economic benefits by introducing an advanced concept for low carbon generation by formulating CPEED with RES. The ABWO computing algorithm was evaluated using uni-model and multi-model benchmark functions, and it has been shown to outperform the existing algorithms in terms of convergence speed and computational budget. By utilizing RES for electricity generation, the cost of fuel and toxic air emissions were significantly reduced. RES penetration in traditional fuel-based plants not only helped in protecting the earth atmosphere but also reduced the dependency on imported fossil fuels which further aids to handle geopolitical problems. ABWO can assist the power generation companies to control emissions imposed by international environmental protection agencies operating globally. The adoption of renewable energy will also aid in the development of the economy and creation of jobs in green energy manufacturing and installation. Despite the fact that the ABWO achieves satisfactory dispatch results, there are some limitations to this study. As a starting point, it is essential to further enhance the precision and integration security of the ABWO optimization technique by incorporating device-level limitations of plants. ABWO has limited flexibility to these constraints, and it is difficult to simulate them. As these features will strengthen the grids reliability and transparency, and they also make it easier to deal with the cyber threats. Second, the CPEED problem does not account sufficiently for the effects of energy storage system, that is, probabilistic loads of plug-in electric automobiles, and it is complex to model driver behavior and storage system constraints in current framework. The implementation of ABWO algorithms for complex problems under various parameters, like population size, crossover rate, mutation rate, and cannibalism rate, requires parametric tuning procedures. Identification of viable solutions for these parameters is currently a challenging research issue for a scientific problem. In the future, we will also use RETScreen to assess the price of RES with storage for grid operation, such as electric mobility vehicles. Future studies will also combine the four-season climate database with different wind speeds or solar irradiance to precisely model the uncertainty of renewable sources.